analysis of the u.s. department of energy’s energy ......of north carolina at greensboro), lori...

s

s

s

Analysis of the US Department of Energyrsquos

Energy Efficiency amp Renewable Energy and Fossil Energy

SBIR Programs

Final Report

February 2019

Prepared by Sabrina T Howell New York University Stern

School of Business

Prepared for Office of Energy Efficiency and Renewable Energy

US Department of Energy

This document was prepared as an account of work sponsored by the US Department of Energy an agency of the United States government Neither the United States government nor any agency thereof nor any of their employees makes any warranty express or implied or assumes any legal liability or responsibility for the accuracy completeness usefulness or any information apparatus product or process disclosed or represents that its use would

not infringe privately owned rights Reference herein to any specific commercial product process or service by trade name trademark manufacturer or otherwise does not necessarily

constitute or imply its endorsement recommendation or favoring by the United States government or any agency thereof The views and opinions of authors expressed herein do

not necessarily state or reflect those of the United States government or any agency thereof

This report uses data from the US Census Bureau Any opinions and conclusions expressed

herein are those of the author and do not necessarily represent the views of the US Census Bureau All results have been reviewed to ensure that no confidential information is disclosed The Disclosure Review Board release numbers are CMS requests 6768 and 7276 for DMS

project 988

Acknowledgements

The research underlying this report was possible because of the determined efforts of Yaw

Agyeman Ken Alston Jeff Dowd Matthew Dunne Carla Frisch Carl Hebron Tina Kaarsshyberg Teryn Norris and Jamie Vernon all currently or formerly at the Department of Energy In particular Tina Kaarsberg provided tireless support and SBIR information Matt Dunne

overcame substantial administrative hurdles in the final phases of data access Carl Heshybron took a great deal of time from his SBIR office responsibilities to provide the data itself Charles Wessner (Georgetown University) Irwin Feller (Penn State) Albert Link (University

of North Carolina at Greensboro) Lori Lewis (Analytical Evaluation Consultants LLC) and Robin Gaster (Innovation Ecologies Inc) provided review of an earlier 2015 analysis of SBIR patenting that is the basis for part of this report Finally the author is grateful to her PhD advisors Joseph Aldy (Harvard Kennedy School) Raj Chetty (Harvard University) David Scharfstein (Harvard Business School) and especially Josh Lerner (Harvard Business School) whose ideas and support have been central to this project from its inception

The Census analysis was undertaken jointly with J David Brown of the US Census Bureau I am very grateful to him to Shawn Klimek and to others at the Census Bureau who made

this part of the analysis possible

ii

Contents

1 Introduction 4

11 Economic Rationale for Analysis 4

12 Background on the SBIR Program at DOE 5

13 Data Description and Summary Statistics 6

131 SBIR Data 6

132 Patent Data 10

133 US Census Bureau Data 11

2 Methods 16

21 Regression Discontinuity Design 16

211 Estimating Equation for Patenting 17

212 Estimating Equations for Firm Growth 18

22 Specification Tests 20

3 The Effect of SBIR Grants on Patenting 22

31 Main Effect of the Phase 1 Grant 22

32 Variation in the Phase 1 Effect on Patents 25

33 The Phase 2 Grant Effect on Patenting 27

4 The Effects of SBIR Grants on Firm Growth 30


42 The Effect of the Phase 1 Grant on Average Wages 36

43 Variation in the Phase 1 Effect on Firm Growth 39

44 The Phase 2 Grant Effect on Firm Growth 41

5 Conclusion 41

6 Appendix Geography and Firm Clustering 43

iii

List of Tables

1 Summary Statistics of DOErsquos EERE and FE SBIR Applicants 10

2 Summary Statistics of SBIR data Matched to US Census Data (1995-2013) 13

3 Phase 1 Grant Effect on Cite-Weighted Patents 24

4 Variation in the Phase 1 Grant Effect on Patenting 26

5 Variation in the Phase 1 Grant Effect by Number of Firmrsquos Previous SBIR

Awards 27

6 Phase 2 Grant Effect on Patenting 29

7 Phase 1 Grant Effect on Firm Growth Outcomes 32

8 Grant Effect on Firm Growth Outcomes Within Two Years of Grant Application 36

9 Phase 1 Grant Effect on Wages 38

10 Variation in the Phase 1 Grant Effect on Firm Growth Outcomes 40


iv

List of Figures

1 Number of Applicants to EERE amp FE Phase 1 SBIR Program 7


3 Average Number of Awards per Competition by Technology Areas 1983-2013 9

4 Continuity in Observable Covariates 21

5 Phase 1 Effects on Cite-Weighted Patents by Rank Around the Award Cutoff 22

6 Phase 1 Effects on Firm Growth by Rank Around the Award Cutoff 34

7 Phase 1 Quarterly Effects on Firm Growth 35

8 Phase 1 Effects on Firm Wages by Rank Around the Award Cutoff 37

9 Phase 1 Quarterly Effects on Firm Wages 37

10 Applicant Firm Locations 43

11 Number of Applicants by Award Status and Metro Area 44

12 Solar and Wind SBIR Firms 45

13 Oil Natural Gas and Coal SBIR Firms 46

14 Wind and Solar VC Portfolio Companies 47

15 SBIR Applicant Firms with at Least 1 VC Deal 48

16 Concentration of Clean Tech-Investing VC Firms 49

v

Executive Summary

This report analyzes the impact of the Department of Energyrsquos (DOE) Small Business Innoshyvation Research (SBIR) program which awards research and development (RampD) grants to

small firms Specifically it examines two applied programs at DOE Energy Efficiency and

Renewable Energy (EERE) and Fossil Energy (FE) With detailed application and ranking

data between 1995 and 2013 it uses a regression discontinuity design to establish a causal relationship between the grants and recipient outcomes

Background

Although governments around the world use grants to subsidize private sector RampD there

is relatively little rigorous quantitative evidence about these subsidiesrsquo effectiveness This study focuses on the Congressionally-mandated SBIR program which provides grants across the US government to small businesses that are developing new commercially viable techshynologies The SBIR program consists of two phases Firms first apply for a (up to $150000

in 2013) Phase 1 award and then Phase 1 winners are eligible to apply for a (up to $1 million

in 2013) Phase 2 award

Study Purpose and Scope

The purpose of this report is to summarize the authorrsquos analysis of DOErsquos EERE and FE

SBIR programs which was conducted to evaluate the impact of the grants on recipients within these programs The report focuses on specific firm outcomes innovation (measured

using patents) employees payroll revenue and wages Howell (2017) also examines the

grant effects on applicantsrsquo subsequent venture capital financing One goal of the SBIR proshygram is commercialization While revenue is used here as a proxy for commercial activity data on technology deployment were not available Patenting activity in addition to providshying a measure of innovation is also viewed by the DOE as an intermediate outcome towards achieving the commercialization objective For an evaluation of the DOE SBIR programrsquos operation the reader is directed to NAS (2016)

Methods

This report uses data on over 4500 firms that applied to all competitions for SBIR awards in

the two applied RampD offices at DOE EERE and FE between 1995 and 2013 The applicant firms are matched to patent data from the US Patent and Trademark Office and to firm

growth data from the US Census Bureau This report draws from analysis in Howell (2017)

1

and from forthcoming work with J David Brown of the US Census Bureaursquos Center for Economic Studies

The estimation strategy for SBIR Phase 1 data called a regression discontinuity design makes use of the ranks that DOE assigns to firms within competitions It compares firms ranked immediately above and below the award cutoff These just-winners and just-nonshywinners are shown to be ex-ante similar such that comparing them is equivalent to a local random experiment In this way the regression discontinuity design enables causal effects to

be established As there are few Phase 2 applicants per competition the Phase 2 analytical approach is a conventional regression - essentially a difference of means with no control for rank If non-winning firms are on average lower quality than winning firms this will bias the Phase 2 results towards finding positive effects

Results

The $150000 Phase 1 award has powerful effects on innovation and firm growth Receiving

a Phase 1 grant increases a firmrsquos subsequent patents weighted by future citations by about 250 percent relative to an average of 12 cite-weighted patents Note that it is not possible to

tie a firmrsquos patents to the specific research that the firm conducted with its SBIR grant We

do not observe how firms use the grant nor do we observe the technologies underlying the

patents The estimation strategy permits us to conclude that the grant causes the difference

in patenting between winning and non-winning applicants

The effect of the Phase 1 grant on patenting is larger among young firms and among firms that have no or few existing patents It also declines in the number of previous SBIR awards a firm has won for each additional previous SBIR award the effect of an award on cite-weighted patents declines by 20 percent

The $1 million Phase 2 grant doubles a firmrsquos cite-weighted patents relative to a mean of 20 This effect is much smaller than the Phase 1 effect on a per-grant-dollar basis Also the

effect of Phase 2 falls and loses significance when the sample includes previous winners

There are also multiple positive Phase 1 effects related to firm growth including wages employment and payroll Using a subset of the applicant firms that were matched to US Census Bureau data the Phase 1 grant is shown to lead firms to have about 19 percent more employees relative to the year of application than they would have had otherwise The

similar result for payroll is 29 percent and the effect on revenue is 15 percent The grant also increases firm wages by about 11 percent Like the effects on innovation the Phase

2

1 grant effect is larger for younger firms It is also larger for smaller firms The Phase 2

grant was not found to have any statistically significant effects on firm employment payroll revenue or wage growth Note that as with the patent data it is not possible to tie specific

employees or portions of revenue to the SBIR grant Again the estimation strategy permits us to identify the effects on firm growth as being caused (perhaps indirectly) by the SBIR

grant

3

1 Introduction

This section first discusses the economic rationale for evaluating the DOE SBIR program and then states the primary research questions Section 12 provides background on the SBIR

program at DOE and Section 13 describes the data that are used Section 14 analyzes how

the applicant firms cluster geographically

11 Economic Rationale for Analysis

Governments worldwide subsidize new high-tech ventures in an effort to spur innovation The largest single grant program for high-tech entrepreneurs in the US is the SBIR program which disbursed around $25 billion in 20171 In addition to the 11-agency federal SBIR many US states have similar programs including New Mexico Ohio and Hawaii Parallels overseas include the UKrsquos Innovation Investment Fund Chinarsquos Innofund Israelrsquos Chief Scientist incubator program Germanyrsquos Mikromezzaninfonds and ZIM Finlandrsquos Tekes Russiarsquos Skolkovo Foundation and Chilersquos InnovaChile Many of these programs deliberately

mimic the US SBIR program

One rationale for such subsidies is that the private sector does not internalize the social benefits of innovation2 Another is that financial frictions lead to underinvestment in early

stage RampD (Hall and Lerner 2010) Grants might increase investment if given to startups that face excessively costly external finance For example it is extremely difficult for potential investors to conduct due diligence on new energy technologies Early stage startups also

often do not have collateralizable assets with which to raise debt Yet critics contend that government RampD subsidies may be ineffective because they displace private investment that would have occurred in the absence of the subsidy or because they allocate funds inefficiently

(Wallsten 2000 Lerner 2009)

Despite opposing theoretical arguments there is relatively little empirical evidence about the effectiveness of direct RampD subsidies3 Existing research has mainly studied non-US

1See httpswwwsbirgovreportsstate-summaryyear5B5D=2017 2For evidence that startups contribute disproportionately to economic growth see Akcigit and Kerr

(2011) Haltiwanger et al (2013) and Audretsch Keilbach and Lehmann (2006)3There is substantial work on RampD tax credits including Hall (1993) Mamuneas amp Nadiri (1996) Hall

amp Van Reenen (2000) Bloom Griffith and Van Reenen (2002) Clausen (2009) Rao (2016) Wu (2008) Wilson (2009) Dechezleprecirctre Einiouml Martin Nguyen amp Van Reenen (2016) and Balsmeier Kurakina amp Fleming (2018)

4

programs and come to disparate conclusions such as Lerner (2000) Wallsten (2000) Lach

(2002) Takalo Tanayama and Toivanen (2013) and Almus and Czarnitzki (2003)4 The

challenge has been that in the absence of a randomized experiment it is difficult to identify

a counterfactual What would happen to the firms if they didnrsquot get the grants This report details an approach that approximates a random experiment comparing applicant firms on

either side of the award cutoff while controlling for the rank that determines their award

status

12 Background on the SBIR Program at DOE

The SBIR program has two ldquoPhasesrdquo Phase 1 grants fund proof-of-concept work intended to

last nine months and are up to $150000 (in 2013 the amount has increased stepwise from

$50000 in 1983 to $200000 in 2019) Firms must demonstrate progress on their Phase 1

projects to win the up to $1 million Phase 2 grants in 2013 The Phase 2 grant size has also

increased stepwise from $500000 to $1100000 in 2019 over the life of the program Phase

2 funds more extensive or later stage demonstrations and the money is awarded over two

years5

Congress first authorized the SBIR program in 1982 to strengthen the US high technology

sector and support small firms6 As of 2017 11 federal agencies must allocate 32 percent of their extramural RampD budgets (ie RampD carried out by non-federal scientists with federal funds) to the SBIR program There is no required private cost sharing in the SBIR program Also the government neither takes equity in the firm nor assumes intellectual property (IP) rights7 Eligible applicants are for-profit US-based and at least 51 percent American-owned firms with fewer than 500 employees Although the SBIR grant is non-dilutive it is

4Evaluations of RampD subsidies include Czarnitzki and Lopes-Bento (2012) Serrano-Velarde (2008) Bushysom (2000) Duguet (2004) Gonzaacutelez et al (2005) Gonzaacutelez and Pazoacute (2008) Blasio Fantino and Pellegrini (2014) and Henningsen et al (2014) In the US Nemet and Kammen (2007) find little evidence of crowdshying out in federal energy RampD but Popp and Newell (2009) do Link and Scott (2010) use SBIR Phase 2 awardee survey data to analyze the likelihood of commercialization To my knowledge only the working papers by Zhao and Ziedonis (2013) and Bronzini and Iachini (2011) use data on applicants to RampD incentive programs The former evaluates a Michigan loan program (N=104) and the latter grants to large firms in Northern Italy (N=171) Both programs have private cost sharing which SBIR does not Other researchers have used RD to evaluate grants to university researchers such as Jacob and Lefgren (2011) and Benavente et al (2012)

5Phase 3 is commercialization of the technology It is ineligible for SBIR funds except when agencies are purchasing the technology which does not occur at DOE but is common at the Department of Defense

6For more background on the origin of SBIR see httpswwwsbirgovbirth-and-history-of-the-sbirshyprogram

7However If the private company does not explicitly protect its inventions the government can exert march-in rights ( 35 USC sect203 in Bayh-Dole Act PL 96-517)

5

not costless In a few interviews conducted by the author investors and startup founders described the application and reporting process as onerous Applying for an SBIR grant can

require two months of 1-2 employees working full time

The DOE assigns SBIR responsibility to program offices responsible for a broad technology

area Two of the applied RampD offices in DOE are Fossil Energy (FE) and Energy Efficiency

and Renewable Energy (EERE)8 Each year DOE officials in these technology-specific proshygram offices develop a series of competitions For example the Solar Energy Technologies office which is responsible for all solar power research including very large grants to national laboratories and universities is also responsible for developing topics and then evaluating

SBIR applicants to those topics

An applicant firm must propose a project that fits with a specific competitionrsquos scope Examshyples of competitions include ldquoSolar Powered Water Desalinationrdquo and ldquoImproved Recovery

Effectiveness In Tar Sands Reservoirsrdquo The empirical strategy in this study compares firms within competitions Applications are evaluated according to three criteria 1) Strength

of the scientifictechnical approach 2) Ability to carry out the project in a cost-effective

manner and 3) Commercialization impact (Oliver 2012) Program officials rank applicants within each competition based in part on written expert reviews from scientists at National Labs universities and the private sector Program officials submit the rank ordered lists to

an independent separate DOE SBIR office The cutoff within each competition is unknown

to the program officer when she produces the rankings The number of awards varies across competitions

13 Data Description and Summary Statistics

131 SBIR Data

This study begins with data on all applicants to the EERE and FE offices for the entirety of the DOE SBIR program from 1983 to 2013 Applicants to the Small Business Technology

Transfer (STTR) program are excluded The data include 7436 small energy technology

8Besides EERE and FE the other offices that received DOE-SBIR funding during the period examined were Office of Science Nuclear Energy Environmental Management and Electricity Delivery amp Energy Reliability DOErsquos ARPA-Emdashstood up in 2009 - manages its own SBIR program During the estimation period (1995-2013) there were several major reorganizations and re-namings of various offices including subunits of EERE Within EERE the nine program offices are now Solar Energy Technology Bioenergy Technologies Fuel Cell Technologies Geothermal Technology Wind Energy Technologies Water Power Technologies Vehicle Technology Building Technology and Advanced Manufacturing

6

firms Figure 1 shows the number of applicants to the EERE amp FE Phase 1 SBIR Program

and annual Phase 2 applicants are shown in Figure 2 The spike in 2010 is due to the

American Recovery and Reinvestment Act (Stimulus) Ranking information is available

only from 1995 so estimation starts in that year The ranks and non-winning applicant identities are confidential and not disclosed to the public9

Figure 1 Number of Applicants to EERE amp FE Phase 1 SBIR Program

Note This figure shows the number of non-winning and winning Phase 1 grant applicants over time Note that firms may appear more than once

9It is only in the authorrsquos capacity as an unpaid DOE employee that she was able to use this data Throughout the paper specific references to companies will only include winners

7



Table 1 contains summary statistics about the applications and competitions for EERE

and FE SBIR Each Phase 1 competition has on average 11 applicants with a standard

deviation of eight The number of awards also varies by competition the average is 17 The number of awards is determined by program budget constraints recent funding history office commitments to projects such as large National Laboratory grants and the overall number of ranked applicants the central DOE SBIR office receives (the number of applicants deemed ldquofundablerdquo)10 The cutoff used to separate winners from non-winners is exogenous to

the ranking process used by DOE The number of SBIR awards does not systematically vary

by any covariate (such as by program office technology area or time)11 Figure 3 shows that there are no obvious differences among technology areas in the average number of awards12

10The number of competitions or subtopics per program officetopic are deliberately designed to scale with the program budget

11The authorrsquos understanding of the exogeneity of the cutoff to the ranking comes from conversations with stakeholders in the DOE SBIR program and from historical email records containing rank submissions

12See Howell (2017) for the average number of applicants per competition by program office the number of awards per office and the number of awards per competition over time

8

Figure 3 Average Number of Awards per Competition by Technology Areas 1983-2013

Note This figure shows that within competitions the average number of Phase 1 awards does not vary systematically across program offices All DOE EERE amp FE competitions from 1995 are included Capped lines indicate 95 confidence intervals

Among applicant firms 71 percent applied only once and a further 14 percent applied twice However a small subset of firms apply and win many times Seven companies in the data

each submitted more than 50 applications However the majority of firms in the data apply

only once and meet general criteria for startups The firm median age is six years and

many firms are less than a year old Among the 23 solar firms that have ever had an IPO nine appear in the data SBIR winners include Sunpower First Solar and Evergreen Solar (Cleantech Group i3) Although there is no strict definition of ldquostartuprdquo they must be

young small and have location-unconstrained growth potential (This is why restaurants plumbers and other local small businesses are not startups) In this context they are also

high-tech

9

Table 1 Summary Statistics of DOErsquos EERE and FE SBIR Applicants

Panel A Application Data from DOE (EERE and FE) 1983-2013

Phase 1 Applications 14522

Unique Phase 1 Applicant Firms 7419

Competitions 1633

1995-2013



Phase 1 Applications with ranking data used in RD 5021

Phase 1 Competitions used in RD ( 1 award) 428

Average Phase 1 Applicants per Competition 11 (83) Average Phase 1 Awards per Competition 17 (11)

Phase 2 Applications used in RD 919

Panel B Variables Used in Analysis from Non-DOE Sources Type Mean Std Dev Median N

Pre-award cite-weighted patents Count 21 122 0 5021 Pre-award patents Post-award cite-weighted patents

(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021

Post-award patents Count 2 11 0 5021 Probability in major metro area (top 6) 0-1 030 046 0 5021 Age (years) Count 95 11 6 3427 Probability tech is hardware (Hardware

i

) 0-1 043 049 1 2571 Prob new sub-sector (Emerging Sector

i

) 0-1 058 049 1 2571 Probability minority owned 0-1 0077 027 0 1722 Probability woman owned 0-1 0084 028 0 1722 All-govrsquot SBIR wins (SBIR

i

) Count 10 36 0 5021 Future patents in modal class Count 9758 11809 5453 1583 MSA VC investment 2011 ($ mill) Cont 851 1570 0 4950 MSA median per cap income 2011 ($ thou) Cont 56 14 56 4603

Notes This table summarizes the DOE SBIR application data in panel A and variables used in the regression analysis in panel B Parentheses in Panel A indicate the standard deviation Cite-weighted patents refers to the number of citations for all the firmrsquos patents MSA refers to metropolitan statistical area

132 Patent Data

This study uses the number of patents and cite-weighted patents as proxies for innovation13

Patenting activity is an imperfect measure of innovation this is only one way that firms 13

Cite-weighted patents refers to the number of citations for all the firmrsquos patents

10

protect their intellectual property and patents have an ambiguous relationship with technoshylogical progress (eg Arora Ceccagnoli and Cohen 2008) Nonetheless they are positively

associated with economic value creation and stock market returns (Hall Jaffe and Trajtenshyberg 2005 Eaton and Kortum 1999) They are also the only currently available quantitative

innovation metric

This study uses patent data from Berkeleyrsquos Fung Institute for Engineering Leadership which includes all patents filed between 1976 and 2014 Utility patents are matched to

applicant firms and checked by hand It is assumed that when a firm cannot be matched

to the patent data the firm has no patents The pre- and post-treatment variables use the

patent application date rather than the issue date as is standard in the literature This is because we are interested in when the invention occurs rather than the grant date which

is on average a few years later To control for patent quality cite-weighted patents (patents weighted by their future citations as in Aghion et al (2013) and Bloom Schankerman and

Van Reenen (2013)) are used The patent count is not normalized by classification or year because competition fixed effects control for sub-sector and date

Note that it is not possible to tie a firmrsquos patents to the specific research that the firm

conducted with its SBIR grant We do not observe how firms use the grant nor do we

observe the technologies underlying the patents The estimation strategy permits us to

conclude that the grant causes the difference in patenting between winning and non-winning

applicants

133 US Census Bureau Data

The second analysis employs outcome measures from US Census Bureau data which was gathered for research with the US Census Bureau Center for Economic Studies The Census Bureau maintains an annual Business Register containing all business establishments in the

US private non-farm sector with at least one employee Applicant firms were matched to

the Business Register by EIN (when available) or probabilistically on name address and zip

code About 70 percent of firms were matched successfully The matching process erred on

the side of including only matches with high confidence to minimize false positives While it is important to note that the matched sample is not the same as the whole sample there is no

correlation of the matching with technology area or other observables so there is no reason

to believe that bias exists Firms were also linked to other Census Bureau business datasets including IRS W-2 data These permit information about employee wages ethnicity and

imputed education levels among other variables

11

The Business Register-matched firms were then linked to the Longitudinal Business Database

(LBD) which begins in 1976 and ends in 2015 The LBD is the universe of non-farm non-public administration business establishments with paid employees This study employs three outcome variables from the LBD The first is employment which is observed in the

pay period that includes March 12 from 1976 until 2005 when employment is observed for all four quarters of each year The second is payroll which is observed quarterly throughout The third is revenue which is observed annually starting in 1996 W-2 data on annual earnings for each employee begin in 2005 and end in 2013 Capital gains or other types of income besides wages are not observed The wage should be thought of as salary income as most of the jobs in this sample are full-time jobs Note that as with the patent data it is not possible to tie specific employees or portions of revenue to the SBIR grant Again the

estimation strategy permits us to identify the effects as being caused (perhaps indirectly) by

the SBIR grant

The highest paid individual in the first year that the firm appears in the LBD is identified

as the ldquofirm founderrdquo This is only a rough approximation but it is in line with other studies using Census data which do not identify firm owners or founders (eg Kerr and Kerr 2017 Azoulay et al 2018 Babina and Howell 2018)

The main summary statistics for the matched data are presented in Table 2 There are 2100

unique applicant firms in 270 competitions with adequate time series data for us to include

in the analysis Some firms apply more than once so there are 4300 applications Wages payroll and revenue are in thousands of real 2010 dollars Variables are summarized at the

annual firm panel level as this is the level of analysis This includes for example industries because industry assignations may change over time within a firm Industry is based on

six-digit NAICS codes Where a firm has multiple units and therefore potentially multiple

industries the NAICS associated with the firmrsquos largest employment share is used

12

Table 2 Summary Statistics of SBIR data Matched to US Census Data (1995-2013)

Panel A SBIR Phase 1 competition data (counts)

N

Unique applicant firms 2100

Applications 4300

Grant award winners 800

Grant award non-winners 3600

Competitions 270

Panel B Outcome and control variables (firm-year level data)

Outcome variables N Mean Std Dev

Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217

Award amountemploymentt=_1 30500 21880 33690

Average wage (rsquo000 2010 $) 30500 6415 3855 Revenue (rsquo000 2010 $) 13000 4834 11410

Log payroll growth (base is t = -1 ) 30500 -0105 1245

Log employment growth (base is t = -1 ) 30500 -0082 1008

Log wage growth (base is t = -1 ) 30500 -0023 0825

Log revenue growth (base is t = -1 ) 13000 -0048 1078

Key control variables N Mean Std Dev

Firm age 30500 1238 8539

Subsequent patent citations (3 year window) 30500 2071 1081

Never previously won an award 30500 057

13

Panel C Additional firm-year variables

Probability in industry (most common 3 digit NAICS) N Mean

Administrative and Support Services Chemical Manufacturing

Computer and Electronic Product Manufacturing

Electrical Equipment Appliance and Component Manufacturing Fabricated Metal Product Manufacturing

Machinery Manufacturing

Merchant Wholesalers Durable Goods

30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257

Professional Scientific and Technical Services 30500 0622

Worker-related variables N Mean Std Dev

Worker age Average worker tenure Share employees who are female

Share employees who are Asian

Share employees who are Black

Share employees who are Hispanic

Share employees who are White

Share employees with BAadvanced degree

Share employees with some college

Share employees with high school degree

Share employees with no high school degree

Share employees who are US born

9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14

Panel D Firm founder and worker-related firm-level variables

Employment in application year Firm age in application year

N

2000

2000

Mean

3331

8309

Std Dev

2564

6378

Average founder wage in application year Average founder age in application year

2000 2000

136000 5128

259300 1214

Share founders female

Share founders Asian

Share founders Black or Hispanic

Share founders white

2000

2000

2000

2000

0115

0168

00383

0797

Share founders US born

Share founders Eastern EuropeRussia born

Share founders Western Europe born

Share founders India born

Share founders China born

2000

2000

2000

2000

2000

0718

00363

00457

0056

00688

Note This table shows summary statistics about the SBIR data that were matched to US Census data Growth measures in Panel B use the year before the application year as the base year denoted t = -1 where year t is the application year The application year is first application year if the firm never won a grant and first winning year if it ever won Panel C contains the share of firms in the most common eight 3-digit NAICS codes are shown (there are a total of 99 3-digit NAICS) Firms may change NAICS codes across years Worker-related variables in Panel C are from linked W-2-Individual Characteristics File data ldquoWhiterdquo indicates non-Hispanic White Panel D contains observations at the firm level and where worker information is available (ie linked to W-2-Individual Characteristics File data) The number of observations rounded to meet Census disclosure requirements

For the matched SBIR-Census data the average number of employees is 35 This is relatively

similar to all firms in the US In 2012 the average for all US firms is 20 employees and

within establishments with 20-99 employees the average is 3914 Average revenue is $48 million though the distribution is highly right skewed The median (unreported due to

disclosure limitations) is much lower The average is also well-aligned with US averages which are $779000 for firms with less than 20 employees and $79 million for firms with

20-99 employees Average payroll in the data is higher than the average for US firms with

20-99 employees at $25 million relative to $16 million

The primary outcome variables are logged growth measures defined as the log difference of an outcome in a given year relative to the year before application (t = -1) Growth

it =

14httpswwwcensusgovdatatables2012econsusb2012-susb-annualhtml

15

ln

Yit

Logs are used to linearize the relationship between the independent (right-hand

Yit=1

side) and dependent (left-hand side) variables in the regression The underlying outcome

data (dependent variables) are positively skewed with a small number of observations that have large magnitudes Taking the log smooths the distribution

Table 2 Panel B shows that on average these measures are near zero but negative That is size measures are larger in the year before application compared to other years Note

that years both before and after the application year are included so the negative mean is consistent with a firms growing before the application and sometimes failing afterward Note

that the standard deviations are very large On average payroll in a given year is about 90

percent of its value in the year before application employment 92 percent wages 98 percent and revenue 95 percent

Additional firm and worker characteristics are in Table 2 Panels C and D As might be

expected for applicants to an RampD grant program the most common NAICS 3-digit industry

is Professional Scientific and Technical Services at 62 percent of firms The next most common is Computer and Electronic Product Manufacturing at 79 percent The table

shows an additional seven industries The average worker is 43 years old while in the

application year the average founder is 51 years old Just 22 percent of employees are

female and only 115 percent of founders are female There are also disparities relative to

the population in ethnic makeup only 27 percent of employees and 38 percent of founders are Black for example Seventy-one percent of employees and founders are US-born Among

founders the next most common country of origin is China with seven percent of founders

2 Methods

This section presents the estimation strategy which is called a regression discontinuity design

(Section 21) It also describes specification tests (Section 22)

21 Regression Discontinuity Design

The estimation strategy relies on the ranks that DOE assigns to firms within competitions It is assumed that firms ranked near the award cutoff are quite similar and after validatshying this assumption with a litany of tests comparing just-winners to just-non-winners is a

16

local random experiment The use of the ranks in this manner is an econometric estimashytion strategy called a sharp regression discontinuity (RD) design As public agencies resist randomizing treatment to evaluate RampD subsidies (unlike new medicines) RD is the best approach to approximating an experiment (Jaffe 2002) The RD design estimates a local average treatment effect around the cutoff in a rating variable Here the rating variable is the applicantrsquos rank The critical assumption is that applicants cannot precisely manipulate

their rank near the cutoff 15

Since the number of applicants and awards varies across competitions applicant ranks are

centered in each competition around zero at the cutoff The lowest-ranked winner has censhytered rank R

i = 1 and the highest-ranked non-winner has Ri = -1 Each competition

has at least this pair As the bandwidth expands higher ranked winners and lower ranked

non-winners are included Controls for rank eliminate ex-ante differences between winners and non-winners that may exist further away from the cutoff (for example if higher ranked

firms tend to be higher quality) In this context however rank turns out to be entirely

uninformative about outcomes so it is immaterial for the results what bandwidth is used

211 Estimating Equation for Patenting

The patent analysis uses variants of Equation 1 where Yi Post is the outcome and dependent

variable The unit of observation is a firm i in a competition j

Post Prev Yij = crarr + fAward

ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)

Following Aghion Van Reenen and Zingales (2013) the regression models focus on cite-weighted patents as an outcome (Y

ij Post ) and use negative binomial and ordinary least

squares (OLS) regression models The coefficient of interest is f on an indicator for receiving

a grant award (treatment) The pre-assignment outcome variable is Yi Prev A level rather

15More specifically a valid RD design must satisfy four conditions to be considered a local randomized experiment First it is necessary that treatment (a grant award) not lead to the rank assignment This holds for the DOE SBIR program as the award happens after ranking To avoid contamination applicants who previously won a grant within EEREFE are excluded Second the cutoff must be exogenous to rank which is true in my setting Third the functional form must be correctly specified or else the estimator will be biased A goodness-of-fit test shows that rank is uninformative Finally to meet the key assumption that applicants cannot precisely manipulate their rank in the region around the cutoff all observable factors must be shown to be locally continuous To establish the necessary weak smoothness (see Hahn et al 2001) continuity of covariates is demonstrated below For more on RD and the above tests see Lee and Lemieux (2010) and Howell (2017)

17

than a change model (where the dependent variable would be Y Post - Y Prev ) is preferred ij i

because it does not confound zero patenting with a zero difference between patents before

and after the application Also it makes it easier to interpret the coefficients of interest and

to compare treatment effects in linear and non-linear (here binomial) models

An SBIR winner is assigned the indicator Awardij = 1 and a non-winner is assigned

Awardij = 0 f (R

ij ) is a polynomial controlling for the firmrsquos rank within competition

c (As elsewhere only first-time winners are included) Rank is limited to various bandshywidths around the cutoff There is a full set of dummies for each competition

j which

controls for the date and a narrow sub-sector Xi indicates other controls16 The estimations

use OLS for binary dependent variables negative binomial for count data and two-part models for semi-continuous data17 Standard errors are robust and clustered by topic-year to account for correlation in time and sector

212 Estimating Equations for Firm Growth

For the firm growth measures a panel data structure is used where firms are observed over time The panel approach exploits the richness of the US Census data permitting finer controls The unit of observation is now a firm i in year t in competition j The primary

empirical specification for evaluating the effect of a grant award is shown in Equation 2

Git = fP ostAward

ijt + Awardij + P ost

ijt (2)

+ f (Rij ) + 3Agei + 4Age

2 i

+ ji +

t + ijt

A firm that ever wins a grant is assigned the non-time varying indicator Awardij = 1

The variable Postijt is an indicator for the year being after the year the firm applied and

P ostAwardijt is the interaction between Post

ijt and Awardij As with patenting winning

16The RD design does not require conditioning on baseline covariates but doing so can reduce sampling variability Lee and Lemieux (2010) advise including the pre-assignment dependent variable as they are usually correlated In tests reported in Howell (2017) rank is projected on observable covariates Previous non-DOE SBIR awards are the strongest predictor of rank A one standard deviation increase in previous SBIR wins (the mean is 114 and the standard deviation is 38) increases the rank by nearly one unit Previous VC deals also have a small positive impact These two variables are included in my primary specifications

17OLS for binary outcomes is used because many of the groups defined by fixed effects (competitions) have no successes (eg no subsequent patents) Logit drops the groups without successes Also OLS with a binary variable is common in applied economics following the arguments in Angrist (2001) that regression does as well as logit in estimating marginal effects and often better with binary treatment variables My main results are intact with a logit specification (see Section 36)

18

firms are included only once Similar results are observed in a non-panel setting like that in

Howell (2017) where each observation is an application rather than a firm-year

In most cases the dependent variable is a log growth measure ln

Yit

where Y

it isYit=1

for example employment or the average wage Some specifications use levels (Yit

) in parshyticular when comparing effects on new and incumbent employees (ldquochangerdquo is undefined for new employees) The growth specification increases power and ensures that unobserved

time-invariant characteristics are controlled for which is a conservative approach since specshyifications with narrow bandwidths around the cutoff are not reported due to disclosure

limitations However all of the results are qualitatively robust to using narrow bandwidths around the cutoff and as shown below rank does not predict outcomes at all as in Howell (2017)

The primary specification controls for rank within the competition quadratically which

means that rank and rank squared are included as covariates This approach includes comshypetition fixed effects (

j as in Equation 1) and calendar year fixed effects t

Other controls

include the firmrsquos age and its age squared Beyond the primary model two other specificashytions are shown for the main effects One controls for rank separately among winners and

non-winners A second includes firm-application fixed effects ( i

) which subsume rank and

control more completely for pre-treatment differences including all the characteristics of the

application Errors are clustered by competition though the main effects are robust to a

variety of error assumptions

Graphed results are presented from two additional specifications that demonstrate robustness to alternative approaches The first shows the effects by rank around the cutoff for the award

using Equation 3

x=3

Yit =

X fx (P ostAward

ij ) (Rankij = x) (3)

x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt

Outcomes are in levels (eg log employment) to provide evidence that the findings from

Equation 2 go through with levels The effects are similar when growth outcomes are used

in Equation 3 instead

The second shows the effects by quarter around the award quarter using Equation 4 where

19

q denotes the quarter

x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq

The equation includes indicators (x

) for 13 and more quarters before the award date each

quarter between 12 and two before the award date the award quarter each quarter after the award date through 12 quarters after and 13 and more quarters after the award quarter The coefficients of interest f

x

are on these same indicators interacted with the award

dummy and these are shown in the graph The equation includes firm-application fixed

effects which are the most stringent specification possible as they control for all possible

application and firm characteristics Again outcomes are in levels There are similar effects using competition fixed effects or growth outcomes In estimating both Equations 3 and 4 standard errors are clustered by competition

22 Specification Tests

A rich array of specification and robustness tests are contained in Howell (2017) Crucial tests are discussed here

A data limitation is that because competitions average only ten applicants the rating varishyable is somewhat discrete This discreteness means that we may worry about jumps in

quality between ranks If there were hundreds of applicants within each competition we

would expect the quality difference between adjacent ranks to be very small Lee and Card

(2008) note that discrete rating variables can require greater extrapolation of the outcomersquos conditional expectation at the cutoff The fundamental econometrics are no different than

with a continuous rating variable however as extrapolation is required in both cases Howshyell (2017) demonstrates the robustness of my findings to this discreteness by for example separately considering competitions with low or high numbers of awards

In order for the estimation strategy to be close to an experiment it is important that firms seem to be equivalent immediately around the cutoff One way to test for similarity around

the cutoff is to examine whether observable characteristics are ldquosmoothrdquo (do not jump) around the cutoff Howell (2017) demonstrates smoothness in observable baseline covariates in three ways through an RD on baseline covariates through differences in means and

20

most importantly visually Figure 4 shows the visual evidence of the baseline covariate RD Baseline covariates include pre-assignment outcome variables average age as well as the

probability a firm is located in a major metro area is woman owned and is minority owned In none of the figures is there any discontinuity around the cutoff visible nor is there any

trend in rank A ninth covariate previous non-DOE SBIR wins is the exception in terms of rank trends When applicants have more previous wins they tend to have a higher rank Nonetheless again there is no discontinuity around the cutoff

Program officials observe more data than the econometrician so it is impossible to fully test the assumption that firms are randomly assigned on either side of the cutoff Nonetheless this preponderance of evidence suggests the RD design is valid

Figure 4 Continuity in Observable Covariates

Notes This figure shows covariates at the award date 95 confidence intervals shown The confidence interval is not included for the probability a firm is minority owned at the 3rd rank from the cutoff because it is very large

21

3 The Effect of SBIR Grants on Patenting

31 Main Effect of the Phase 1 Grant

The best available measure for innovation is patenting Though patents are not the only way

that firms protect IP they are positively associated with economic value creation and stock

market returns (Hall Jaffe and Trajtenberg 2005) Figure 5 shows log cite-weighted patents before and after the Phase 1 grant award (all matching patents are included so there is no

time restriction around the award) The figure clearly indicates a strong effect of the award we see a large jump around the cutoff for patents filed after the award Comfortingly there

is no such jump around the cutoff before the award indicating that the estimation strategy

is valid Ranks among high-ranking non-winners are predictive of future patenting This relationship disappears for winners

Notes This figure shows ln (1 + Citespost

) before and after the Phase 1 grant award decision using the

i patent application date DOErsquos rank is centered so positive ranks indicate a firm won an award 95 confidence intervals shown

Results from estimating variants of Equation 1 are in Table 3 The bandwidth is one rank

around the cutoff in each competition except in column 3 where all the data with quadratic

rank controls are used In the primary sample of no previous winners and using the negshyative binomial specification an award increases cite-weighted patents by 25 times (panel A columns 1-4)18 The OLS specification finds that the grant increases log cite-weighted

18Coefficients indicate for a one unit increase in regressor the difference in the logs of expected counts

Figure 5 Phase 1 Effects on Cite-Weighted Patents by Rank Around the Award Cutoff

22

patents by about 30 percent (panel B columns 1-3) Note that the linearization reduces the

effect

All patents filed after the competitionrsquos award date are used as competition fixed effects control for years since the grant However the time fixed effects do not necessarily control for when the firm patents In unreported specifications (not shown because of limitations to

the number of estimates that Census will permit to be disclosed) results are similar when

citations are limited to three years after the patent Also the grant increases the probability

of positive patenting by 9 percentage points (pp) Rank has no predictive power over patents in all models

Centering ranks might obscure information in the raw rank For example firms with centered

ranks of two might have different qualities in competitions with two and four awards To

address this Panels A and B column 4 use dummies for the firmrsquos rank quintile within the

competition19 Conditional on award status there is no information in rank visually or in

regressions regardless of bandwidth

Column 5 permits previous winners to be included in the sample As a result the number of observations increases The effect declines somewhat The reason for this decline is highlighted by the two following columns First column 6 limits the sample to first time

applicants and yields a much larger effect than the main sample Conversely when only

firms with more than two wins are included (column 7) the effect declines by more than half Unreported regressions find that for each previous DOE SBIR award the effect of an award

on log cite-weighted patents declines by 20 percent There is a similar pattern for other outcome variables examined in Howell (2017) but it is especially troubling for patenting A

small subset of applicants win many awards and may be dependent on grants While such

firms might naturally not be seeking VC or direct sales if their RampD is productive it should

yield patents Instead there is a a steeply declining patenting benefit of additional grants to

the same firm This supports DOE program officialsrsquo skepticism about permitting so-called

ldquoSBIR millsrdquo to win many awards

If is the Poisson rate ( patents) = log

Ric gt0 Exponentiating gives the incidence rate ratio (how

Ric lt0

many times more patents winners get than non-winners)19There are similar results with the slope controlled for separately on each side of the cutoff (with a

bandwidth of all specification) and using quartile ranks

23

Table 3 Phase 1 Grant Effect on Cite-Weighted Patents

Panel A Negative binomial model dependent variable is Citespost i

Sample Bandwidth

No

1

previous winners 1 All All

All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072

Rank quintiles Controlsdagger

N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N

Sector-year fedaggerdagger

N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2

Panel B OLS models dependent variable is ln (1 + Citespost

)i

No previous winners All No prev apps gt2 prev wins 1 1 All All 1 1 1

(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075

Note This table reports regression estimates of the Phase 1 award effect on cite-weighted patents using variants of Equation 1 The model is negative binomial in panel A and OLS in panel B Columns 1-4 limit the sample to firms that have not previously won an award but may have previously applied Columns 5-7 respectively use the whole sample only firms that have never previously applied and only firms that have more than two previous wins daggerControls are Citesprev or ln (1 + Citesprev

) and previous non-DOE SBIR i i

awards daggerdaggerCompetition fixed effects (fe) in the NB model do not permit convergence Fixed effects mean that a separate control is included for each competition so that the estimation result is within competition Year 1995 Standard errors are clustered by sector-year lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

24

32 Variation in the Phase 1 Effect on Patents

The effect on innovation activity varies with firm characteristics For this analysis the

number of patents is used as this yields sharper results though the effects are qualitatively

similar using cite-weighted patents First it is expected that young firms have fewer internal resources and their RampD investment is likely more affected by capital market imperfections (Hall 2008) Columns 1-4 of Table 4 show that the grant effect on short-term patenting

falls dramatically and loses all significance for older firms (at least 10 years old) The patent incidence rate ratio (IRR) is a staggering 12 for firms no more than two years old statistically

significant at the 1 level (column 1) whereas the IRR is only 175 for firms more than two

years old and is not statistically significant For firms less than 10 years old the IRR is 45 whereas for firms older than 10 it is 062 - a negative effect - and statistically insignificant (columns 3 and 4) This suggests that young privately held firms may face greater RampD

investment financing constraints than older private firms supporting the findings on public

firms in Brown Fazzari and Petersen (2009)

As with age there may be more information available about firms with patents Hsu and

Ziedonis (2008) and Conti Thursby and Thursby (2013) show that patents improve enshytrepreneursrsquo access to finance by signaling potential investors about a firmrsquos quality Patents may also serve as collateral as in Mann (2018) and Hochberg Serrano and Ziedonis (2014) The latter paper finds that among VC-backed startups with patents 36 percent used the

patents to secure loans Columns 5 and 6 of Table 4 shows that the treatment effect declines when firms have previous patents with no patents the grant leads a firm to produce 33

times more patents than it would otherwise With at least one patent the IRR is 27 This decline in the Phase 1 grant effect when a firm has more previous patents may indicate that more experienced later stage firms with better access to debt finance benefit less from the

grants

There is wide variation in propensity to patent across technologies (Scherer 1983 Brouwer and Kleinknecht 1999) Table 4 columns 7 and 8 use an indicator for high propensity to

patent from the USPTO (2012) patent intensity estimations20 In high propensity industries the IRR is 81 statistically significant at the 1 percent level (Table 4 column 7) This means that a grantee produces 81 times as many patents as a non-winner In contrast in low

propensity industries the IRR is only 27 statistically significant at the 10 percent level 20These are based on patents per 1000 jobs in an industry The indicator takes a value of 1 if the firm

is in one of the following sectors Smart Grid Sensors amp Power Converters Advanced Materials Solar or Batteries and 0 otherwise

25

(Table 4 column 8) For all three heterogeneity analyses in Table 4 difference (interaction) equations cannot be estimated because the maximum likelihood function does not converge

Table 4 Variation in the Phase 1 Grant Effect on Patenting

Dependent Variable Patent3 yrs Post i

Sample Firm Age in Years

2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48

(38) (42) (28) (11) Rank and rank2 Y Y Y Y controls Controlsdagger Y Y Y Y

Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367

Firm Previous Patents

0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646

Tech Patent Propensity

High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640

Note This table reports regression estimates of the effect of the Phase 1 grant award on patents using bandwidth (BW)=3 and a negative binomial model focusing on variation by firm age technology propensity to patent and number of previous patents Propensity to patent is calculated as described in the text The specifications are variants of the model in Equation 1 The dependent variable

3 yrs Post Patent is the number of successful patents that the firm applied for within three years of the

i grant award The left panel divides the sample by an indicator for high propensity to patent which is based on overall USPTO 2012 patent intensity by technology It is 1 if the firmrsquos technology sub-sector is Smart Grid Sensors amp Power Converters Advanced Materials Solar or Batteries The middle panel divides the sample by firm age and the right panel by the firmrsquos number of patents prior to applying for the grant For all three difference equations cannot be estimated due to non-convergence of the Poisson maximum likelihood daggerControls are Patentprev and previous non-DOE SBIR awards Standard errors are

i robust p lt 01 Year 1995

Finally Table 5 shows that there may be a precipitous drop in patents by previous non-DOE SBIR wins but the coefficients are somewhat imprecise A bandwidth of all firms is used to maximize the sample size but similar results are found with narrower bandwidths Relatedly Howell (2017) finds a robust and sharp drop in the probability of receiving venture

capital financing in the number of previous non-DOE SBIR awards

26

Table 5 Variation in the Phase 1 Grant Effect by Number of Firmrsquos Previous SBIR Awards

3 yrs Post Dependent Variable Patenti

Sample Number of previous SBIR wins lt 2 lt 5 gt 0 2 5

(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257

(0531) (0481) (0369) (0444) (0576) Rank and rank2 Y Y Y Y Y controls Controlsdagger Y Y Y Y Y

Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101

Note This table shows estimates of the impact of the Phase 1 grant award on the firmrsquos patent count within three years after grant award by number of previous non-DOE SBIR awards (from other government agencies eg DOD NSF) using BW=all and a negative binomial model Each column includes only data from firms with the relevant number of wins Unfortunately difference equations could not be estimated due to non-convergence of the Poisson maximum likelihood daggerControls are Patentprev and previous

i non-DOE SBIR awards Standard errors robust and clustered at topic-year level p lt 1 Year 1995

This supports the idea that efforts to avoid funding ldquoSBIR millsrdquo (firms with substantial recurring revenue from many SBIR awards) may be warranted

33 The Phase 2 Grant Effect on Patenting

About nine months after receiving a Phase 1 award a firm may apply for Phase 2 If successful the firm receives Phase 2 in two increments of $500000 roughly two and three

years after the Phase 1 award Any Phase 2 effect is local to the subset of Phase 1 winners Many Phase 1 competitions have two or fewer Phase 2 applicants so there is no control for rank and only sector and year fixed effects are included Phase 2 is therefore analyzed as though the Phase 2 grants were randomly assigned If contrary to this assumption the DOE

is selecting higher quality applicants to win Phase 2 the results should be biased upward To further clarify this means that the Phase 2 analysis is likely to produce positive results unless DOE is either randomly selecting applicants or selecting lower quality applicants as winners

27

Using the negative binomial model and the standard sample of no previous winners columns 1-3 of Table 6 show that Phase 2 doubles a firmrsquos cite-weighted patents relative to a mean

of 20 Column 3 jointly estimates both Phases The coefficient on Phase 2 drops to about 14 times as many cite-weighted patents OLS results using ln

(1 + Citespost

) in columns 7-9

i

find that Phase 2 increases cite-weighted patents by about 30 percent Over half of Phase

2 applicants have won multiple DOE SBIR awards and so are excluded from the primary

sample Consistent with the Phase 1 findings the positive Phase 2 effect on cite-weighted

patents falls and loses significance when the sample includes previous winners

The Phase 2 grant does generate new inventive activity in the form of cite-weighted patents But its effects are much smaller than Phase 1 on a public dollar basis Phase 1 involves only $150000 but a grant yields about 14 cite-weighted patents The Phase 2 grant is up

to $1000000 and a high estimate for the Phase 2 impact is 2 cite-weighted patents To

illustrate how the per public dollar effect is so much larger for Phase 1 consider the following

example In 2012 the DOE spent $38 million on 257 Phase 1 grants and $112 million on 111

Phase 2 grants If all the Phase 2 money were reallocated to Phase 1 the DOE could have

provided 750 additional firms with Phase 1 grants increasing by a factor of about 25 the

programrsquos impact on cite-weighted patents

Note that this hypothetical is not a policy recommendation and comes with significant caveats One is that discontinuing Phase 2 would likely affect the Phase 1 applicant pool21

In the absence of Phase 2 as a possibility in case the Phase 1 research did not quickly lead

to external private finance prospective applicants might not apply to the program at all Another caveat is as noted in Section 12 Phase 2 funds more extensive or later stage

demonstrations than Phase 1 Phase 2 recipients may shift resources toward commercializashytion efforts and may not continue patenting at a high rate because they are busy working

on commercialization Finally awarding many more Phase 1 grants would require awarding

more lower ranked applicants Although rank is not informative about outcomes (ie lower ranked applicants do not tend to do worse) this would affect the composition of awardees in unknown ways

21According to the EERE SBIR Director there is significant anecdotal evidence (eg Phase I grantees willingness to report on progress well beyond the minimum requirement) that the prospect of a Phase 2 grant is a strong motivation for applying for Phase 1

28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042

Table 6 Phase 2 Grant Effect on Patenting

The Phase 2 sample is small because 37 percent of Phase 1 winners do not apply for Phase 2 There are four reasons for this based on interviews with grantees and DOE officials First a

29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels

firm is ineligible to apply if an outside investor owns more than 50 percent Some firms that raise equity after Phase 1 may sell too much of the firm to be eligible to apply for Phase 2 Consistent with this possibility among firms that receive VC within two years of the Phase

1 grant 55 percent do not apply for Phase 2 Put another way 19 percent of non-Phase 2

applicants received VC investment within two years of their initial Phase 1 award but only

8 percent of Phase 2 applicants did (the statistics on VC can be found in Howell 2017)22

Second firms might not apply if they changed business strategies Phase 1 commercializashytion activities are known to DOE only if a firm applies for Phase 2 where such activities are required to be described Third the Phase 2 application and reporting processes are so

onerous that once a firm receives external private finance it may sometimes not be worthshywhile to apply to Phase 223 Relatedly Gans and Stern (2003) suggest that private funding

is preferred to SBIR funding A firmrsquos discount rate may increase once its initial RampD is successful so that applying to Phase 1 is worthwhile but applying to Phase 2 is not despite

the larger sum at stake This is consistent with the sharply decreasing risk premium in Berk Green and Naik (2004) as an RampD project moves from initiation to completion The adverse

selection in the Phase 2 sample suggests that startups seeking to raise external finance whose

Phase 1 RampD revealed positive information often secured private investment without needshying further government support Fourth the Phase 1 ldquoproof-of-concept researchrdquo may have

failed to prove the concept and firms thus lack a rationale for continued Phase 2 funding

4 The Effects of SBIR Grants on Firm Growth


The effects of the Phase 1 grant award on firm growth (employees payroll revenue and

wages) are presented in Table 7 There are large effects on payroll employment and revenue No effects were found on firm exit via acquisition or failure (unreported due to Census disclosure limitations)24

22A t-test of the difference of means strongly rejects the hypothesis that non-appliers and appliers have the same mean probability of VC investment within two years with a t-statistic of 544

23The time consuming and onerous process was given as a reason for not applying in interviews with grantees and investors

24Exit via acquisition or merger is defined as an instance in which the last establishment year is later than the last firm year This indicates that the establishment continues but the firm dies Failure is defined as establishment and firm exit from the panel

30

The effect of winning a grant on log employment after the application year relative to the

base year is shown as the coefficient on P ostAwardijt in columns 1-3 Put another way

the coefficient is the average effect of winning in years after the application year controlling

for whether the firm is a winning firm and whether the year is after the application year The coefficients on quadratic rank (column 1) and on either side of the cutoff (column 2) are also shown Firm-application fixed effects are included in column 3 which account for most of the controls used elsewhere The coefficient of 027 on P ostAward

ijt indicates that a grant award increases employment relative to the base year by about 30 percent25 We can

also calculate what the coefficient implies for employment growth among winners Winners have about 19 percent more employees than non-winners or on average 67 more employees relative to the year before application

Figure 6 Panel A demonstrates the effect on levels of log employment by rank around the

cutoff This figure provides visual evidence that there is a significant effect of the grant as we see a jump at the cutoff for the award Figure 7 Panel A demonstrates the effect on levels of log employment by quarter around the award quarter This shows that the effect is quite

immediate

The effect on payroll in columns 4-6 (where the coefficients are 036-04) indicates a roughly

43 percent increase in payroll relative to the base year26 This translates to at the mean 29

percent more payroll than in the pre-application year Figure 6 Panel B demonstrates the

effect on levels of log payroll by rank around the cutoff and Figure 7 Panel B demonstrates the effect on levels of log payroll by quarter around the award quarter The effect on revenue

in columns 7-9 indicates a 20 percent increase in revenue relative to the base year or 15

percent more revenue than in the pre-application year Figure 6 Panel C demonstrates the

effect on levels of log revenue by rank around the cutoff There is no quarterly graph because

Census does not have quarterly revenue data 25The coefficient gives the percentage change in Yit associated with being an award recipient relative

Yit=1 (to a non-winner The exact effect is 100 (e -1) Note it is relative to the year before the application (that

is the effect is not an absolute increase) 26The coefficient gives the percentage change in Yit associated with being an award recipient relative


is the effect is not an absolute increase)

31

Table 7 Phase 1 Grant Effect on Firm Growth Outcomes

Dependent variable Employment Payroll Revenue

(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award

ij - - - Y Y N Y Y N

Postijt - - N Y Y Y Y Y Y

Rankij Rank2

ij - N N Y N N Y N N

Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y

Competitionj Y Y N Y Y N Y Y N

Firm-applicationij N N Y N N Y N N Y

N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528

Note This panel shows the effect of the grant on log growth outcomes using Equation 2 The base year for the dependent variables is t = -1 the year before the application year Control coefficients are shown only for employment and no other outcomes to minimize disclosure requirements None of these variables have any statistical significance in subsequent columns Data are observed at the firm-year level Standard errors are clustered by competition lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

The effect is quite similar across the three specifications shown in Table 7 The results are

32

also robust to a number of unreported approaches (unreported because Census disclosure

requirements limit the number of estimates we may release) First they are similar using

narrow years around the application Second the results go through with a bandwidth of one firm around the cutoff Third when the sample is split roughly in half around either 2005 or 2008 there are similar effects on either side The magnitude of the effect is somewhat larger in the early period (ie before 2005 or 2008) but not statistically significantly so

The effects occur quickly Table 8 shows that about half the effect on employment occurs within two years of the grant application and just more than half for payroll and revenue The large magnitude of the effects relative to the size of the grant (just $150000) implies that the grant is invested in productive activities

33

s

s

Figure 6 Phase 1 Effects on Firm Growth by Rank Around the Award Cutoff

Panel A Employment

Panel B Payroll

Panel C Revenue

Note These figures show the results from estimating Equation 3 on levels of log firm-year employment payroll and revenue Each point is a coefficient on a specific DOE-assigned rank around the award cutoff where positive ranks are winning applicant firms and negative ranks are non-winning applicant firms 95 confidence intervals are shown

34

s

s

Figure 7 Phase 1 Quarterly Effects on Firm Growth

Panel A Employment

Panel B Payroll

Note These figures show the results from estimating Equation 4 on quarterly levels of log firm-year employshyment and payroll Each point is a coefficient on a quarter around the award quarter interacted with winning an award The base quarter is -1 (immediately before the quarter of award) There are no quarterly revenue or employee-level wage (and thus inequality) data 95 confidence intervals are shown

35

Table 8 Grant Effect on Firm Growth Outcomes Within Two Years of Grant Application


(1) (2) (3)

P ostAwardijt 142 268 159

(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426

Note This panel shows the effect of the grant on log growth outcomes in the two years after the grant application year (including the application year) using Equation 2 The base year for the dependent variables is t = -1 the year before the application year Control coefficients are shown only for employment to minimize disclosure requirements None of these variables have any significance in subsequent columns Data are observed at the firm-year level Standard errors are clustered by competition lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

42 The Effect of the Phase 1 Grant on Average Wages

There may be interest in the effect of Phase 1 on the firmrsquos average wage Table 9 shows the grant effect on wage growth with the three main specifications based on Equation 2 in

columns 1-3 A grant increases wage growth in the years following the grant by about 10

percent in the most conservative result with firm-application fixed effects (column 3) and 14

percent in the main specification where rank is controlled for quadratically (column 1) (We

control for rank quadratically to account for potential non-linearity in the effect of rank) Using the larger result the Phase 1 effect translates to about an 11 percent increase in the

average wage relative to the pre-application year Figure 8 demonstrates the effect on levels of log wages by rank around the cutoff and Figure 9 shows the effect on levels of log wages by quarter around the award quarter

36

s

Figure 8 Phase 1 Effects on Firm Wages by Rank Around the Award Cutoff

Note This figure show the results from estimating Equation 3 on levels of log firm-year average wages Each point is a coefficient on a specific DOE-assigned rank around the award cutoff where positive ranks are winning applicant firms and negative ranks are non-winning applicant firms Only two positive ranks for inequality are reported because the smaller sample led to a very large confidence interval for the firm three ranks away from the cutoff (which exists in competitions with at least three winners) The coefficient magnitude is in line with the previous two 95 confidence intervals are shown

Figure 9 Phase 1 Quarterly Effects on Firm Wages

Note This figure shows the results from estimating Equation 4 on quarterly levels of log firm-year average wages Each point is a coefficient on a quarter around the award quarter interacted with winning an award The base quarter is -1 (immediately before the quarter of award) 95 confidence intervals are shown

As with the Phase 1 effects on payroll employment and revenue the wage increase occurs

37

quickly Almost the entire effect is observed within two years as shown in column 4 Column

5 of Table 9 shows the wage growth of the firm founder The Phase I grant has a much

larger founder wage effect than average of about 47 percent As the individual perhaps most responsible for the firmrsquos past and future productivity growth and for having won the grant it is not surprising that the founder experiences a larger wage increase

Table 9 Phase 1 Grant Effect on Wages

Dependent variable Wage growth

Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39

(0048) (00481) (00391) (00406) (0206) P ostAwardP erEmp

ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y

AwardP erEmpijt N N N N N Y

Fixed effects Year

t Y Y Y Y Y Y

Competitionj Y Y N Y Y Y

Firm-applicationij N N Y N N N

N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986

Note This panel shows the effect of the grant on wage growth using Equation 2 The base year is t = -1 the year before the application year Columns 1-3 replicate the three main specifications from Table 7 with rank controlled for quadratically on either side of the cutoff or through firm-application fixed effects Column 4 restricts the sample to the two years after the grant application year (including the application year) Column 5 examines the effect of the grant on the wage of the firm founder Column 6 uses an alternative independent variable P ostAwardP erEmp

ijt is P ostAwardijt interacted with the logged grant amount

per employee where the number of employees is identified in year t = -1 Control coefficients are not reported to minimize disclosure requirements Data are observed at the firm-year level Except in column 5 wage is the computed as the average wage within the firm-year Standard errors are clustered by competition lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

38

43 Variation in the Phase 1 Effect on Firm Growth

For all firm growth outcomes potential variation in the effect was tested for a wide array of firm firm founder industry and state characteristics The only robust variation is shown in

Table 10 The grant is more useful for smaller and younger firms consistent with the findings for patenting shown above A similar result was found for venture capital in Howell (2017) Columns 1 and 4 show the effect of winning a grant award interacted with log employment in the year before the grant application The effect is large and negative indicating that firms that are larger at the time of application experience a smaller effect

Columns 2 and 5 similarly show that the effects of a grant on firm employment and payroll are decreasing in firm age at the time of application Columns 3 and 6 interact winning

with an indicator for a firm not having previous SBIR grants from other agencies (Recall that only first-time winners are included in the panel data so it is not possible to consider previous DOE SBIR wins) The effect on the interaction is large and negative albeit not statistically significant The three characteristics in this table (size age and previous wins) operate in the same direction for wage growth but are statistically insignificant There is no

heterogeneity whatsoever on within-firm inequality

It is worth mentioning key tests that yielded no statistically significant interaction effects There is no effect of founder gender age tenure or raceethnicity nor is there heterogeneity

in the share of employees of a certain gender age bin tenure bin or raceethnicity There

is also no effect by worker tenure

39

Table 10 Variation in the Phase 1 Grant Effect on Firm Growth Outcomes

Dependent variable Employment Payroll

(1) (2) (3) (4) (5) (6)

P ostAwardijt middot Employment

t=_1 -167 -201

(00942) (00832) P ostAward

ijt middot Aget=_1 -0315 -0339

(00135) (00131) P ostAward

ijt middot NoP revW inst=_1 -0226 -0115

(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y

Awardij middot Employment

t=_1 Y N N Y N N

Postijt middot Employment

t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N

Awardij middot NoP revW ins

t=_1 N N Y N N Y

Postijt middot NoP revW ins

t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y

Competitionj Y Y Y Y Y Y

N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214

Note This table shows the effect of the grant interacted with firm characteristics on log growth outcomes using Equation 2 The base year for the dependent variables is t = -1 the year before the application year Employment

t=_1 is log employment in year t = -1 Aget=-1 is firm age in years in year t = -1 and

NoP revW inst=-1 is an indicator for the firm not having previously won an SBIR award from a non-DOE

agency Control coefficients are not reported to minimize disclosure requirements Data are observed at the firm-year level Standard errors are clustered by competition lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

40

44 The Phase 2 Grant Effect on Firm Growth

The Phase 2 grant was not found to have any statistically significant effects on firm growth These null results are shown in Table 11 The coefficients are statistically insignificant and

considerably smaller than the effects of the Phase 1 grant As with patenting because the

sample is small rank controls and competition fixed effects are omitted The results are

similar with these additional controls or when Phase 1 and 2 are considered in the same

regression As explained in Section 33 the small sample and insignificant effects may reflect adverse selection in firmsrsquo decisions to apply to Phase 2



(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272

Note This panel shows the effect of the Phase 2 grant on log growth outcomes using Equation 2 The base year for the dependent variables is t = -1 the year before the application year Control coefficients are not reported to minimize disclosure requirements Data are observed at the firm-year level Standard errors are clustered by competition lowast lowastlowast and lowastlowastlowast denote significance at the 10 5 and 1 levels

5 Conclusion

To the authorrsquos knowledge this study offers the first large sample analysis of RampD subsidies to private firms that establishes a truly causal relationship between the grants and firm

outcomes in large part because ranked application data for a RampD subsidy program has

41

never before been made available for research This study may offer government agencies around the world a template for rigorous external analysis of their RampD subsidy programs

This study demonstrates that US DOE Phase 1 SBIR grants have large positive effects on firm innovation growth and average wages In particular receiving a Phase 1 grant award increases a firmrsquos subsequent patents weighted by future citations by about 25 times relative to an average of 12 The $1 million Phase 2 grant doubles a firmrsquos cite-weighted

patents relative to a mean of 20 This Phase 2 effect is much smaller than the Phase 1 effect on a per-grant-dollar basis Also the effect of Phase 2 falls and loses significance when the

sample includes previous winners

The Phase 1 grant also leads firms to have about 19 percent more employees relative to the

year of application than they would have had otherwise The similar result for payroll is 29 percent and the effect on revenue is 15 percent The grant also increases firm wages by

about 11 percent The Phase 2 grant was not found to have any effects on firm employment payroll revenue or wage growth

The Phase 1 grants are useful because they enable proof-of-concept or prototyping work The firms that seem to benefit most from the SBIR Phase 1 are startups With a successshyful proof-of-concept a startup can show to investors that its technology concept is valid A second avenue is certification the governmentrsquos decision might convey positive informashytion to venture capitalists about the firmrsquos technology For additional discussion about the

mechanism see Howell (2017) provides additional discussion and evidence about the grantsrsquo mechanisms However future research could more affirmatively establish how grants are

useful to firms at what stage in the firm lifecycle and for what types of firms

One reason for the effectiveness of Phase 1 is that applicant firms may be financially conshystrained For early-stage projects in general it is difficult to access conventional small business bank loans and prospective equity investors have substantial uncertainty about a

technologyrsquos potential Further energy technology startups are more capital intensive have

longer lead times and carry higher project finance and market risk than the startups VCs typically finance in IT and biotech (Nanda Younge and Fleming 2013) Finally commershycializing clean energy technologies is challenging in the absence of a carbon price (Nordhaus 2013) All these reasons help explain why the grants do not appear to crowd out private

investment The importance of some of these factors could be tested by applying this type of analysis to other agenciesrsquo SBIR programs such as those of the National Institutes of Health

or the National Science Foundation

42

6 Appendix Geography and Firm Clustering

The geography of SBIR applicants corresponds to what might be expected of high-tech

firms Figure 10 shows the applicant concentrations by Metropolitan Statistical Area (MSA) Applicant locations were geocoded using address information The largest clusters by far are

Boston and San Francisco and to a lesser extent New York Los Angeles DenverBoulder and the greater DC metro area

Figure 10 Applicant Firm Locations

Note This figure shows the location of all applicant firms in the data In the main figure a darker color for a metropolitan statistical area (MSA) indicates higher firm density In the insets actual firm locations are overlaid as orange dots

The number of applicants by award status from the top ten metro regions with the highest number of applicants in 1995 and in 2012 are in Figure 11 San Francisco moves from 9th

place to 1st place reflecting its growing role in the energy technology sector LA and Boston

are near the top of the list in both years Bridgeport-Stamford and Baltimore fall completely

43

off the list while NYC and DC enter This suggests that the changing agglomerations of SBIR winners over time may reflect citiesrsquo lifecycles Graphs by year not shown here suggest that the concentration has not changed much over time except for the San Francisco area which has increased in importance since the mid-1990s

Figure 11 Number of Applicants by Award Status and Metro Area

Note This figure shows the number of applicants by award status (whether they won or lost) from the top 10 EEREFE SBIR applicant metropolitan statistical areas

Wind and solar applicants (categorized by the competition topic area) are in Figure 12 and oil gas and coal applicants in Figure 13 It is clear that although both renewable

and conventional fuel companies locate in major cities some clustering relates to the arearsquos resource base Clusters of coal companies in Pittsburgh PA and oil and gas companies in

Houston TX contrast with clusters of solar firms in Tampa FL and Orlando FL

44

Figure 12 Solar and Wind SBIR Firms

Note This figure shows the location of unique EEREFE SBIR applicant firms in the solar and wind industries

45

Figure 13 Oil Natural Gas and Coal SBIR Firms

Note This figure shows the location of unique EEREFE SBIR applicant firms in the oil natural gas and coal industries

Figure 12 shows the location of all wind and solar companies that venture capital (VC) firms have invested in based on the ThompsonOne database Agglomeration in Boston and San

Francisco and to a lesser degree in New York Denver and Chicago are common to both

the SBIR applicants (Figure 16) and the VC portfolio companies (Figure 14) in these clean

energy sectors However the portfolio companies are more concentrated in the major cities where VC firms are also located We can see this by examining the location of applicants with at least one VC deal (Figure 15) and comparing to the concentration by MSA of all active VC firms in the Preqin database that according to Preqin invest in clean technology

(Figure 16)

46

Figure 14 Wind and Solar VC Portfolio Companies

Note This figure shows the location of unique VC-backed firms in the solar and wind industries from the ThompsonOne database

47

Figure 15 SBIR Applicant Firms with at Least 1 VC Deal

Note This figure shows the location of unique EEREFE SBIR applicant firms that have at least one VC deal

48

Figure 16 Concentration of Clean Tech-Investing VC Firms

Note This figure shows the location by metropolitan statistical area of VC firms that invest in clean technology using the whole Preqin database

In general the clustering of both VC firms and VC-funded DOE SBIR applicants aligns fairly closely with the clustering of the overall applicant pool However there is considerably

more clustering of both VC firms and VC-funded companies in Boston and San Francisco especially VC-funded companies that have received many VC investment rounds Meanwhile there are far more SBIR applicants from Los Angeles and from the greater DC metro area

than portfolio companies For the subset of firms that focus their resources on government grants and procurement contracts the DC concentration makes sense Los Angeles also seems be a long-term hub of government-oriented tech companies For example Physical Optics - the largest SBIR winner in my data - is located in Torrance CA within the Los Angeles MSA The Los Angeles government provides supporting activities such as regular workshops on applying for SBIR grants and other informational and convening resources through its PortTech Los Angeles program Los Angeles Regional Small Business Development Center and others

49

repreneurship20_20Integratedpdf

References

Aghion P Van Reenen J amp Zingales L (2013) Innovation and institutional ownership Amershyican Economic Review 103(1) 277-304

Akcigit U amp Kerr W R (2018) Growth through heterogeneous innovations Journal of Political Economy 126(4) 1374-1443

Almus M amp Czarnitzki D (2003) The effects of public RampD subsidies on firmsrsquo innovation activities The case of eastern Germany Journal of Business amp Economic Statistics 21(2) 226-236

Angrist J D (2001) Estimation of limited dependent variable models with dummy endogenous regressors Simple strategies for empirical practice Journal of Business amp Economic Statistics 19(1) 2-16

Arora A Ceccagnoli M amp Cohen W M (2008) RampD and the patent premium International Journal of Industrial Organization 26(5) 1153-1179

Audretsch D B Keilbach M C amp Lehmann E E (2006) Entrepreneurship and economic growth Oxford University Press

Azoulay P Jones B Kim J D amp Miranda J (2018) Age and high-growth entrepreneurship Working paper available at httpssieprstanfordedusystemfilesAge20and20High20Growth20Ent

Babina T amp Howell S T (2018) Entrepreneurial spillovers from corporate RampD (No w25360) National Bureau of Economic Research available at httpswwwnberorgpapersw25360

Benavente J M Crespi G Figal Garone L amp Maffioli A (2012) The impact of national research funds A regression discontinuity approach to the Chilean FONDECYT Research Policy 41(8) 1461-1475

Berk J B Green R C amp Naik V (2004) Valuation and return dynamics of new ventures Review of Financial Studies 17(1) 1-35

Blasio G Fantino D amp Pellegrini G (2011) Evaluating the impact of innovation incentives Evidence from an unexpected shortage of funds Industrial and Corporate Change 23(5) 1-30

Bloom N Griffith R amp Van Reenen J (2002) Do RampD tax credits work Evidence from a panel of countries 1979ndash1997 Journal of Public Economics 85(1) 1-31

Bloom N Schankerman M amp Van Reenen J (2013) Identifying technology spill-overs and product market rivalry Econometrica 81(4) 1347-1393

Busom I (2000) An empirical evaluation of the effects of RampD subsidies Economics of Innovashytion and New Technology 9(2) 111-148

Bronzini R amp Iachini E (2014) Are incentives for RampD effective Evidence from a regression discontinuity approach American Economic Journal Economic Policy 6(4) 100-134

50

Brouwer E amp Kleinknecht A (1999) Innovative output and a firmrsquos propensity to patent An exploration of CIS micro data Research Policy 28(6) 615-624

Brown J R Fazzari S M amp Petersen B C (2009) Financing innovation and growth Cash flow external equity and the 1990s RampD boom Journal of Finance 64(1) 151-185

Clausen T H (2009) Do subsidies have positive impacts on RampD and innovation activities at the firm level Structural Change and Economic Dynamics 20(4) 239-253

Conti A Thursby J amp Thursby M (2013) Patents as signals for startup financing Journal of Industrial Economics 61(3) 592-622

Czarnitzki D amp Bento C L (2012) Evaluation of public RampD policies A crossndashcountry comparison World Review of Science Technology and Sustainable Development 9(2) 254shy282

Dechezleprecirctre A Einiouml E Martin R Nguyen K T amp Van Reenen J (2016) Do tax incentives for research increase firm innovation An RD design for RampD (No w22405) National Bureau of Economic Research

Duguet E (2004) Are RampD subsidies a substitute or a complement to privately funded RampD Revue drsquoeacuteconomie politique 114(2) 245-274

Eaton J amp Kortum S (1999) International technology diffusion Theory and measurement International Economic Review 40(3) 537-570

Gans J S amp Stern S (2003) The product market and the market for ldquoideasrdquo Commercialization strategies for technology entrepreneurs Research Policy 32(2) 333-350

Gonzaacutelez X Jaumandreu J amp Pazoacute C (2005) Barriers to innovation and subsidy effectiveness RAND Journal of Economics 930-950

Gonzaacutelez X amp Pazoacute C (2008) Do public subsidies stimulate private RampD spending Research Policy 37(3) 371-389

Hahn J Todd P amp Van der Klaauw W (2001) Identification and estimation of treatment effects with a regression-discontinuity design Econometrica 69(1) 201-209

Hall B H (1993) RampD tax policy during the 1980s Success or failure Tax Policy and the Economy 7 1-35

Hall B H (2008) The financing of innovation In S Shane (Ed) Handbook of Technology and Innovation Management (pp 409-430) Oxford Blackwell Publishers Ltd

Hall B H Jaffe A amp Trajtenberg M (2005) Market value and patent citations RAND Journal of Economics 16-38

Hall B amp Van Reenen J (2000) How effective are fiscal incentives for RampD A review of the evidence Research Policy 29(4-5) 449-469

Haltiwanger J Jarmin R S amp Miranda J (2013) Who creates jobs Small versus large versus young Review of Economics and Statistics 95(2) 347-361

51

Henningsen M S Haeliggeland T amp Moslashen J (2015) Estimating the additionality of RampD subshysidies using proposal evaluation data to control for research intentions Journal of Technology Transfer 40(2) 227-251

Hochberg Y V Serrano C J amp Ziedonis R H (2018) Patent collateral investor commitment and the market for venture lending Journal of Financial Economics 130(1) 74-94

Howell S T (2017) Financing innovation Evidence from RampD grants American Economic Review 107(4) 1136-64

Hsu D H amp Ziedonis R H (2008) Patents as quality signals for entrepreneurial ventures In Academy of Management Proceedings (Vol 2008(1) pp 1-6) Briarcliff Manor NY Academy of Management

Jacob B A amp Lefgren L (2011) The impact of research grant funding on scientific productivity Journal of Public Economics 95(9-10) 1168-1177

Jaffe A B (2002) Building programme evaluation into the design of public research-support programmes Oxford Review of Economic Policy 18(1) 22-34

Kerr S P amp Kerr W R (2016) Immigrant entrepreneurship In J Haltiwanger E Hurst J Miranda amp A Schoar (Eds) Measuring Entrepreneurial Businesses Current Knowledge and Challenges (pp 187-249) University of Chicago Press

Lach S (2002) Do RampD subsidies stimulate or displace private RampD Evidence from Israel Journal of Industrial Economics 50(4) 369-390

Lee D S amp Card D (2008) Regression discontinuity inference with specification error Journal of Econometrics 142(2) 655-674

Lee D S amp Lemieux T (2010) Regression discontinuity designs in economics Journal of Economic Literature 48(2) 281-355

Lerner J (2000) The government as venture capitalist The long-run impact of the SBIR proshygram Journal of Private Equity 3(2) 55-78

Lerner J (2009) Boulevard of broken dreams Why public efforts to boost entrepreneurship and venture capital have failedndashand what to do about it Princeton University Press

Link A N amp Scott J T (2010) Government as entrepreneur Evaluating the commercialization success of SBIR projects Research Policy 39(5) 589-601

Mamuneas T P amp Nadiri M I (1996) Public RampD policies and cost behavior of the US manufacturing industries Journal of Public Economics 63(1) 57-81

Mann W (2018) Creditor rights and innovation Evidence from patent collateral Journal of Financial Economics 130(1) 25-47

Nanda R Younge K amp Fleming L (2013) Innovation and entrepreneurship in renewable enshyergy In A Jaffe amp B Jones (Eds) The changing frontier Rethinking science and innovation policy University of Chicago Press

52

1927404amprep=rep1amptype=pdf

Nemet G F amp Kammen D M (2007) US energy research and development Declining investshyment increasing need and the feasibility of expansion Energy Policy 35(1) 746-755

Nordhaus W D (2013) The climate casino Risk uncertainty and economics for a warming world Yale University Press

National Academies of Sciences Engineering and Medicine (NAS) (2016) SBIRSTTR at the Department of Energy Washington DC The National Academies Press

Oliver M (2012) Overview of the DOErsquos Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs DOE Webinar November 30 2012

Popp D amp Newell R (2012) Where does energy RampD come from Examining crowding out from energy RampD Energy Economics 34(4) 980-991

Rao N (2016) Do tax credits stimulate RampD spending The effect of the RampD tax credit in its first decade Journal of Public Economics 140 1-12

Scherer F M (1983) The propensity to patent International Journal of Industrial Organization 1(1) 107-128

Serrano-Velarde N (2008) The financing structure of corporate RampD Evidence from regression discontinuity design Working paper available at httpciteseerxistpsueduviewdocdownloaddoi=1011

Takalo T Tanayama T amp Toivanen O (2013) Estimating the benefits of targeted RampD subsidies Review of Economics and Statistics 95(1) 255-272

US Department of Commerce (2012) Intellectual Property and the US Economy Industries in Focus Retrieved from httpwwwusptogovnewspublicationsIP_Report_March_2012pdf

Wallsten S J (2000) The effects of government-industry RampD programs on private RampD The case of the Small Business Innovation Research program The RAND Journal of Economics 82-100

Wilson D J (2009) Beggar thy neighbor The in-state out-of-state and aggregate effects of RampD tax credits Review of Economics and Statistics 91(2) 431-436

Wu Y (2008) State RampD tax credits and high-technology establishments Economic Developshyment Quarterly 22(2) 136-148

Zhao B amp Ziedonis R H (2012) State governments as financiers of technology startups Implishycations for firm performance Working paper available at SSRN httpsssrncomabstract=2060739

53

This document was prepared as an account of work sponsored by the US Department of Energy an agency of the United States government Neither the United States government nor any agency thereof nor any of their employees makes any warranty express or implied or assumes any legal liability or responsibility for the accuracy completeness usefulness or any information apparatus product or process disclosed or represents that its use would

not infringe privately owned rights Reference herein to any specific commercial product process or service by trade name trademark manufacturer or otherwise does not necessarily

constitute or imply its endorsement recommendation or favoring by the United States government or any agency thereof The views and opinions of authors expressed herein do

not necessarily state or reflect those of the United States government or any agency thereof

This report uses data from the US Census Bureau Any opinions and conclusions expressed

herein are those of the author and do not necessarily represent the views of the US Census Bureau All results have been reviewed to ensure that no confidential information is disclosed The Disclosure Review Board release numbers are CMS requests 6768 and 7276 for DMS

project 988

Acknowledgements

The research underlying this report was possible because of the determined efforts of Yaw

Agyeman Ken Alston Jeff Dowd Matthew Dunne Carla Frisch Carl Hebron Tina Kaarsshyberg Teryn Norris and Jamie Vernon all currently or formerly at the Department of Energy In particular Tina Kaarsberg provided tireless support and SBIR information Matt Dunne

overcame substantial administrative hurdles in the final phases of data access Carl Heshybron took a great deal of time from his SBIR office responsibilities to provide the data itself Charles Wessner (Georgetown University) Irwin Feller (Penn State) Albert Link (University

of North Carolina at Greensboro) Lori Lewis (Analytical Evaluation Consultants LLC) and Robin Gaster (Innovation Ecologies Inc) provided review of an earlier 2015 analysis of SBIR patenting that is the basis for part of this report Finally the author is grateful to her PhD advisors Joseph Aldy (Harvard Kennedy School) Raj Chetty (Harvard University) David Scharfstein (Harvard Business School) and especially Josh Lerner (Harvard Business School) whose ideas and support have been central to this project from its inception

The Census analysis was undertaken jointly with J David Brown of the US Census Bureau I am very grateful to him to Shawn Klimek and to others at the Census Bureau who made

this part of the analysis possible

ii

Contents

1 Introduction 4




131 SBIR Data 6

132 Patent Data 10


2 Methods 16














5 Conclusion 41


iii

List of Tables






Awards 27







iv

List of Figures

















v

Executive Summary





Background










Methods




1




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

Contents

1 Introduction 4




131 SBIR Data 6

132 Patent Data 10


2 Methods 16














5 Conclusion 41


iii

List of Tables






Awards 27







iv

List of Figures

















v

Executive Summary





Background










Methods




1




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

List of Tables






Awards 27







iv

List of Figures

















v

Executive Summary





Background










Methods




1




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

List of Figures

















v

Executive Summary





Background










Methods




1




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

Executive Summary





Background










Methods




1




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53




Results












2




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53




grant

3

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

1 Introduction















4






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53






status








years5







5














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53














131 SBIR Data




6








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53








7











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53











8








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53








high-tech

9





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53





Competitions 1633

1995-2013









(Citespost

i

) Count Count

19 12

75 117

0 0

5021 5021


i


i


i



132 Patent Data




10



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



innovation metric











applicants










11





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53





the SBIR grant









12



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



N


Applications 4300



Competitions 270



Payroll (rsquo000 2010 $) 30500 2546 6141

Employment 30500 3536 7217








Firm age 30500 1238 8539



13








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53








30500

30500

30500

30500

30500

30500

30500

0013

00167

0079

00324

00241

00495

00257













9600

9600 9600

9600

9600

9600

9600

9600

9600

9600

9600

9600

431

2219 0223

0173

00273

00406

0737

0515

0250

0169

00642

0714

8398

1925

14



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



N

2000

2000

Mean

3331

8309

Std Dev

2564

6378


2000 2000

136000 5128

259300 1214





2000

2000

2000

2000

0115

0168

00383

0797






2000

2000

2000

2000

2000

0718

00363

00457

0056

00688









it =


15

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

ln

Yit


Yit=1















2 Methods





16














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53














ij + f (Rij ) + 1Y

ij + 2Xij + j +

ij (1)






17






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53






Awardij = 0 f (R



j which






Git = fP ostAward


ijt (2)


2 i

+ ji +

t + ijt







18




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53




Yit

where Y

it isYit=1








Other controls








using Equation 3

x=3

Yit =

X fx (P ostAward


x=-6

+ 1Agei + 2Age2 i +

t + j +

ijt





19


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53


x=13+

Yiq =

X [f

x (Awardij = 1) (q = x) + x (q = x)] (4)

x=-13

+ q +

i + ijq




x














20







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53







21

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

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017

(01

5)

Y Y 459

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ious

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ners

(7)

(8)

(9)

029

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13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

















22


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

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30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

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5)

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7)

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86

7

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5)

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Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53


effect





















Ric lt0



23



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

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gt1

prev

w

in

(6)

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(01

5)

Y Y 459

010

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post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



Sample Bandwidth

No

1


All 1

No prev apps 1

gt2 prev wins 1

Award

(1) 093

(2) 091 092

(3) (4) 094

(5) 082

(6) 21

(7) 040

Normalized rank

(021) (019) (033) 0052

(026) (013) (034) (014)

Normalized rank2

(0074) -00072


N

N

N

Y

(00045) N

N

Y

N

N

N

N

N

N

N


N

Y

1871

Y

1871

Y

5021

Y

5021

Y

2714

Y

972

Y

1477

R2 0056 0084 0053 0053 0034 0080 0035

Sample Bandwidth

Award

Normalized rank

Normalized rank2


Competition fe N

Pseudo-R2


)i


(1) (2) (3) (4) (5) (6) (7) 033 027 029 022 029 049 022

(015) (013) (011) (0087) (0087) (021) (013) -0026

(002) 78e-4

(00011) N N N Y N N N

N Y N N N N N

Y Y Y Y Y Y Y

1872 1872 5021 5021 2714 972 1477

046 060 053 0351 063 071 075




24














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

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yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

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prev

w

in

(6)

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(01

5)

Y Y 459

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ln

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No

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ious

win

ners

(7)

(8)

(9)

029

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13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53














grants





25





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

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post

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No

prev

ious

win

ners

(7)

(8)

(9)

029

032

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13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53





2 gt 2 9 gt 9

(1) (2) (3) (4) Award 25 56 15 -48


Topic fe N N N N

Year fe Y Y Y Y

N 576 2790 1410 1958

Pseudo-R2 14 092 1 1

Log likelihood -383 -2221 -1220 -1367


0 1

(5) (6) 12 1

(39) (23) Y Y

Y Y

N N

Y Y

2308 1058

083 067

-794 -1646


High Low

(7) (8) 21 99

(46) (22) Y Y

Y Y

Y Y

N N

834 2532

15 2

-719 -1640







26




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

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7)

YN

YY

867

86

7

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2 0

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prev

w

in

(6)

017

(01

5)

Y Y 459

010

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No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53




(1) (2) (3) (4) (5) Award 0896 0805 -0405 0120 -0257


Year fe Y Y Y Y Y

N 4249 4651 1879 1444 1042

Pseudo-R2 0097 0098 0093 0096 0101








27



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

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prev

w

in

(6)

017

(01

5)

Y Y 459

010

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ln

( 1+

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post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



(1 + Citespost

) in columns 7-9

i



















28

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

(01

7)

YN

YY

867

86

7

009

2 0

042

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prev

w

in

(6)

017

(01

5)

Y Y 459

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ln

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Cites

post

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No

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ious

win

ners

(7)

(8)

(9)

029

032

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(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

var

iant

s of

Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

Mod

el

Dep

ende

nt v

aria

ble

Sam

ple

Pha

se 2

Aw

ard

Pha

se 1

Aw

ard

Con

trol

sdagger

Sect

or amp

yea

r fe

N

[Pse

udo-

]R 2

No

prev

ious

win

ners

(1)

(2)

(3)

077

069

034

(0

29)

(0

30)

(0

17)

033

(01

0)

YN

Y

YY

Y

408

40

8

5021

013

0

091

021

Neg

ativ

e bi

nom

ial

Cites

post

i

All

appl

ican

ts

(4)

(5)

028

0

25

(01

5)

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7)

YN

YY

867

86

7

009

2 0

042

gt1

prev

w

in

(6)

017

(01

5)

Y Y 459

010

OLS

ln

( 1+

Cites

post

) i

No

prev

ious

win

ners

(7)

(8)

(9)

029

032

022

(0

13)

(0

15)

(0

12)

017

(00

61)

Y

NY

Y

YY

408

40

8

5021

051

0

35

042



29

Not

e T

his

tabl

e re

port

s es

tim

ates

of t

he P

hase

2 g

rant

eff e

ct o

n al

l out

com

es u

sing

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iant

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Equ

atio

n 1

The

mod

el v

arie

sba

sed

on t

he d

epen

dent

var

iabl

e T

here

is n

o ra

nk c

ontr

ol d

ue t

o th

e sm

all n

umbe

r of

app

lican

ts in

eac

h co

mpe

titi

on

dagger Con

trol

s ar

e ln(1

+ C

ites

prev

) a

nd SBIR

prev

in b

oth

Sta

ndar

d er

rors

clu

ster

ed b

y se

ctor

-yea

r Y

ear

1995

Stan

dard

erro

rsi

i

are

clus

tere

d by

sec

tor-

year

lowast

lowastlowast

and

lowastlowastlowast

deno

te s

igni

fican

ce a

t th

e 10

5

a

nd 1

le

vels



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



















30









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53









immediate













31



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



(1) (2) (3) (4) (5) (6) (7) (8) (9)

P ostAwardijt 271 262 27 406 396 365 19 183 273

(00984) (00968) (00795) (0109) (0107) (00898) (00614) (00613) (00707) Award

ij -0073 00348 0019

(00752) (00889) (00333) Post

ijt -00333 -00321

(00374) (00375) Rank

ij 000352

(000773) Rank2

ij 0000107

(0000189) Rank | win

ij -00584

(0036) Rank | lose

ij 0000996

(00024)

Controls Award



Rankij Rank2


Rank | winloseij

Ageit

Age2 it

N

Y

-Y

N

Y

N

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

Fixed Effects Year

t Y Y Y Y Y Y Y Y Y



N 30500 30500 30500 30500 30500 30500 13000 13000 13000

R2 021 021 0532 0151 0152 0478 0143 0141 0528



32





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53





33

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

s

s


Panel A Employment

Panel B Payroll

Panel C Revenue


34

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

s

s


Panel A Employment

Panel B Payroll


35



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53



(1) (2) (3)


(00573) (00672) (00624) Controls Award

ij Y Y Y

Postijt Y Y Y

Rankij Rank2

ij Y Y Y

Ageit

Age2 it Y Y Y

Fixed Effects Year

t Y Y Y

Competitionj Y Y Y

N 20000 20000 9500

R2 0244 0178 0426









36

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53

s






37






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53






Within 2 years

Of firm founder

(1) (2) (3) (4) (5) (6)

P ostAwardijt 134 133 0946 126 39


ijt 0128

(000519)

Controls Award

ij Y Y N Y Y Y

Postijt Y Y Y Y Y Y

Rankij Rank2

ij Y N N Y Y Y

Rank | winloseij

Ageit

Age2 it

N

Y

Y

Y

N

Y

N

Y

N

Y

N

Y


Fixed effects Year

t Y Y Y Y Y Y



N 30500 30500 30500 20000 1600 30500

R2 00988 0099 0449 00738 0288 00986




38










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



41
















42










43







44



45







(Figure 16)

46



47



48






49


References
















50


















51

















52
















53










39



(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





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(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



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(Figure 16)

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(1) (2) (3) (4) (5) (6)


t=_1 -167 -201

(00942) (00832) P ostAward


(00135) (00131) P ostAward


(0208) (0206) P ostAward

ijt 859 733 364 861 679 255

(0289) (0191) (014) (0256) (0176) (0129) Employment

t=_1 -169 -19

(00241) (00183) Age

t=_1 0167 0079

(00076) (00543) NoP revW ins

t=_1 217 188

(0062) (00473)

Controls Award

ij Y Y Y Y Y Y

Postijt Y Y Y Y Y Y


t=_1 Y N N Y N N


t=_1 Y N N Y N N

Awardij middot Age

t=_1 N Y N N Y N

Postijt middot Age

t=_1 N Y N N Y N


t=_1 N N Y N N Y


t=_1 N N Y N N Y

Rankij Rank2

ij Y Y Y Y Y Y

Ageit

Age2 it Y Y Y Y Y Y

Fixed effects Year

t Y Y Y Y Y Y


N 30500 30500 30500 30500 30500 30500

R2 0189 0175 0175 0244 0214 0214





40









(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



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(Figure 16)

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(1) (2) (3)

P ostAwardijt 00899 0178 -00879

(0193) (0173) (00666)

Controls Award

ij Y Y Y

Postijt Y Y Y

Fixed Effects Year

t Y Y Y

N 3100 3100 3100

R2 0384 0464 0272


5 Conclusion



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analysis of the u.s. department of energy’s energy ......of north carolina at greensboro), lori...

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